Aceleración de algoritmos de procesamiento de imágenes para el análisis de partículas individuales con microscopia electrónica

Tesis Doctoral inédita cotutelada por la Masaryk University (República Checa) y la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Ingeniería Informática. Fecha de Lectura: 24-10-2022Cryogenic Electron Microscopy (Cryo-EM) is a vital field in current structural biology. Unlike X-ray crystallography and Nuclear Magnetic Resonance, it can be used to analyze membrane proteins and other samples with overlapping spectral peaks. However, one of the significant limitations of Cryo-EM is the computational complexity. Modern electron microscopes can produce terabytes of data per single session, from which hundreds of thousands of particles must be extracted and processed to obtain a near-atomic resolution of the original sample. Many existing software solutions use high-Performance Computing (HPC) techniques to bring these computations to the realm of practical usability. The common approach to acceleration is parallelization of the processing, but in praxis, we face many complications, such as problem decomposition, data distribution, load scheduling, balancing, and synchronization. Utilization of various accelerators further complicates the situation, as heterogeneous hardware brings additional caveats, for example, limited portability, under-utilization due to synchronization, and sub-optimal code performance due to missing specialization. This dissertation, structured as a compendium of articles, aims to improve the algorithms used in Cryo-EM, esp. the SPA (Single Particle Analysis). We focus on the single-node performance optimizations, using the techniques either available or developed in the HPC field, such as heterogeneous computing or autotuning, which potentially needs the formulation of novel algorithms. The secondary goal of the dissertation is to identify the limitations of state-of-the-art HPC techniques. Since the Cryo-EM pipeline consists of multiple distinct steps targetting different types of data, there is no single bottleneck to be solved. As such, the presented articles show a holistic approach to performance optimization. First, we give details on the GPU acceleration of the specific programs. The achieved speedup is due to the higher performance of the GPU, adjustments of the original algorithm to it, and application of the novel algorithms. More specifically, we provide implementation details of programs for movie alignment, 2D classification, and 3D reconstruction that have been sped up by order of magnitude compared to their original multi-CPU implementation or sufficiently the be used on-the-fly. In addition to these three programs, multiple other programs from an actively used, open-source software package XMIPP have been accelerated and improved. Second, we discuss our contribution to HPC in the form of autotuning. Autotuning is the ability of software to adapt to a changing environment, i.e., input or executing hardware. Towards that goal, we present cuFFTAdvisor, a tool that proposes and, through autotuning, finds the best configuration of the cuFFT library for given constraints of input size and plan settings. We also introduce a benchmark set of ten autotunable kernels for important computational problems implemented in OpenCL or CUDA, together with the introduction of complex dynamic autotuning to the KTT tool. Third, we propose an image processing framework Umpalumpa, which combines a task-based runtime system, data-centric architecture, and dynamic autotuning. The proposed framework allows for writing complex workflows which automatically use available HW resources and adjust to different HW and data but at the same time are easy to maintainThe project that gave rise to these results received the support of a fellowship from the “la Caixa” Foundation (ID 100010434). The fellowship code is LCF/BQ/DI18/11660021. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 71367

Optimization Techniques for Mapping Algorithms and Applications onto CUDA GPU Platforms and CPU-GPU Heterogeneous Platforms

An emerging trend in processor architecture seems to indicate the doubling of the number of cores per chip every two years with same or decreased clock speed. Of particular interest to this thesis is the class of many-core processors, which are becoming more attractive due to their high performance, low cost, and low power consumption. The main goal of this dissertation is to develop optimization techniques for mapping algorithms and applications onto CUDA GPUs and CPU-GPU heterogeneous platforms. The Fast Fourier transform (FFT) constitutes a fundamental tool in computational science and engineering, and hence a GPU-optimized implementation is of paramount importance. We first study the mapping of the 3D FFT onto the recent, CUDA GPUs and develop a new approach that minimizes the number of global memory accesses and overlaps the computations along the different dimensions. We obtain some of the fastest known implementations for the computation of multi-dimensional FFT. We then present a highly multithreaded FFT-based direct Poisson solver that is optimized for the recent NVIDIA GPUs. In addition to the massive multithreading, our algorithm carefully manages the multiple layers of the memory hierarchy so that all global memory accesses are coalesced into 128-bytes device memory transactions. As a result, we have achieved up to 375GFLOPS with a bandwidth of 120GB/s on the GTX 480. We further extend our methodology to deal with CPU-GPU based heterogeneous platforms for the case when the input is too large to fit on the GPU global memory. We develop optimization techniques for memory-bound, and computation-bound application. The main challenge here is to minimize data transfer between the CPU memory and the device memory and to overlap as much as possible these transfers with kernel execution. For memory-bounded applications, we achieve a near-peak effective PCIe bus bandwidth, 9-10GB/s and performance as high as 145 GFLOPS for multi-dimensional FFT computations and for solving the Poisson equation. We extend our CPU-GPU based software pipeline to a computation-bound application-DGEMM, and achieve the illusion of a memory of the CPU memory size and a computation throughput similar to a pure GPU

Fast algorithm for real-time rings reconstruction

The GAP project is dedicated to study the application of GPU in several contexts in which real-time response is important to take decisions. The definition of real-time depends on the application under study, ranging from answer time of μs up to several hours in case of very computing intensive task. During this conference we presented our work in low level triggers [1] [2] and high level triggers [3] in high energy physics experiments, and specific application for nuclear magnetic resonance (NMR) [4] [5] and cone-beam CT [6]. Apart from the study of dedicated solution to decrease the latency due to data transport and preparation, the computing algorithms play an essential role in any GPU application. In this contribution, we show an original algorithm developed for triggers application, to accelerate the ring reconstruction in RICH detector when it is not possible to have seeds for reconstruction from external trackers

A reference model for integrated energy and power management of HPC systems

Optimizing a computer for highest performance dictates the efficient use of its limited resources. Computers as a whole are rather complex. Therefore, it is not sufficient to consider optimizing hardware and software components independently. Instead, a holistic view to manage the interactions of all components is essential to achieve system-wide efficiency. For High Performance Computing (HPC) systems, today, the major limiting resources are energy and power. The hardware mechanisms to measure and control energy and power are exposed to software. The software systems using these mechanisms range from firmware, operating system, system software to tools and applications. Efforts to improve energy and power efficiency of HPC systems and the infrastructure of HPC centers achieve perpetual advances. In isolation, these efforts are unable to cope with the rising energy and power demands of large scale systems. A systematic way to integrate multiple optimization strategies, which build on complementary, interacting hardware and software systems is missing. This work provides a reference model for integrated energy and power management of HPC systems: the Open Integrated Energy and Power (OIEP) reference model. The goal is to enable the implementation, setup, and maintenance of modular system-wide energy and power management solutions. The proposed model goes beyond current practices, which focus on individual HPC centers or implementations, in that it allows to universally describe any hierarchical energy and power management systems with a multitude of requirements. The model builds solid foundations to be understandable and verifiable, to guarantee stable interaction of hardware and software components, for a known and trusted chain of command. This work identifies the main building blocks of the OIEP reference model, describes their abstract setup, and shows concrete instances thereof. A principal aspect is how the individual components are connected, interface in a hierarchical manner and thus can optimize for the global policy, pursued as a computing center's operating strategy. In addition to the reference model itself, a method for applying the reference model is presented. This method is used to show the practicality of the reference model and its application. For future research in energy and power management of HPC systems, the OIEP reference model forms a cornerstone to realize --- plan, develop and integrate --- innovative energy and power management solutions. For HPC systems themselves, it supports to transparently manage current systems with their inherent complexity, it allows to integrate novel solutions into existing setups, and it enables to design new systems from scratch. In fact, the OIEP reference model represents a basis for holistic efficient optimization.Computer auf höchstmögliche Rechenleistung zu optimieren bedingt Effizienzmaximierung aller limitierenden Ressourcen. Computer sind komplexe Systeme. Deshalb ist es nicht ausreichend, Hardware und Software isoliert zu betrachten. Stattdessen ist eine Gesamtsicht des Systems notwendig, um die Interaktionen aller Einzelkomponenten zu organisieren und systemweite Optimierungen zu ermöglichen. Für Höchstleistungsrechner (HLR) ist die limitierende Ressource heute ihre Leistungsaufnahme und der resultierende Gesamtenergieverbrauch. In aktuellen HLR-Systemen sind Energie- und Leistungsaufnahme programmatisch auslesbar als auch direkt und indirekt steuerbar. Diese Mechanismen werden in diversen Softwarekomponenten von Firmware, Betriebssystem, Systemsoftware bis hin zu Werkzeugen und Anwendungen genutzt und stetig weiterentwickelt. Durch die Komplexität der interagierenden Systeme ist eine systematische Optimierung des Gesamtsystems nur schwer durchführbar, als auch nachvollziehbar. Ein methodisches Vorgehen zur Integration verschiedener Optimierungsansätze, die auf komplementäre, interagierende Hardware- und Softwaresysteme aufbauen, fehlt. Diese Arbeit beschreibt ein Referenzmodell für integriertes Energie- und Leistungsmanagement von HLR-Systemen, das „Open Integrated Energy and Power (OIEP)“ Referenzmodell. Das Ziel ist ein Referenzmodell, dass die Entwicklung von modularen, systemweiten energie- und leistungsoptimierenden Sofware-Verbunden ermöglicht und diese als allgemeines hierarchisches Managementsystem beschreibt. Dies hebt das Modell von bisherigen Ansätzen ab, welche sich auf Einzellösungen, spezifischen Software oder die Bedürfnisse einzelner Rechenzentren beschränken. Dazu beschreibt es Grundlagen für ein planbares und verifizierbares Gesamtsystem und erlaubt nachvollziehbares und sicheres Delegieren von Energie- und Leistungsmanagement an Untersysteme unter Aufrechterhaltung der Befehlskette. Die Arbeit liefert die Grundlagen des Referenzmodells. Hierbei werden die Einzelkomponenten der Software-Verbunde identifiziert, deren abstrakter Aufbau sowie konkrete Instanziierungen gezeigt. Spezielles Augenmerk liegt auf dem hierarchischen Aufbau und der resultierenden Interaktionen der Komponenten. Die allgemeine Beschreibung des Referenzmodells erlaubt den Entwurf von Systemarchitekturen, welche letztendlich die Effizienzmaximierung der Ressource Energie mit den gegebenen Mechanismen ganzheitlich umsetzen können. Hierfür wird ein Verfahren zur methodischen Anwendung des Referenzmodells beschrieben, welches die Modellierung beliebiger Energie- und Leistungsverwaltungssystemen ermöglicht. Für Forschung im Bereich des Energie- und Leistungsmanagement für HLR bildet das OIEP Referenzmodell Eckstein, um Planung, Entwicklung und Integration von innovativen Lösungen umzusetzen. Für die HLR-Systeme selbst unterstützt es nachvollziehbare Verwaltung der komplexen Systeme und bietet die Möglichkeit, neue Beschaffungen und Entwicklungen erfolgreich zu integrieren. Das OIEP Referenzmodell bietet somit ein Fundament für gesamtheitliche effiziente Systemoptimierung

Software for Exascale Computing - SPPEXA 2016-2019

This open access book summarizes the research done and results obtained in the second funding phase of the Priority Program 1648 "Software for Exascale Computing" (SPPEXA) of the German Research Foundation (DFG) presented at the SPPEXA Symposium in Dresden during October 21-23, 2019. In that respect, it both represents a continuation of Vol. 113 in Springer’s series Lecture Notes in Computational Science and Engineering, the corresponding report of SPPEXA’s first funding phase, and provides an overview of SPPEXA’s contributions towards exascale computing in today's sumpercomputer technology. The individual chapters address one or more of the research directions (1) computational algorithms, (2) system software, (3) application software, (4) data management and exploration, (5) programming, and (6) software tools. The book has an interdisciplinary appeal: scholars from computational sub-fields in computer science, mathematics, physics, or engineering will find it of particular interest

Density-Aware Linear Algebra in a Column-Oriented In-Memory Database System

Linear algebra operations appear in nearly every application in advanced analytics, machine learning, and of various science domains. Until today, many data analysts and scientists tend to use statistics software packages or hand-crafted solutions for their analysis. In the era of data deluge, however, the external statistics packages and custom analysis programs that often run on single-workstations are incapable to keep up with the vast increase in data volume and size. In particular, there is an increasing demand of scientists for large scale data manipulation, orchestration, and advanced data management capabilities. These are among the key features of a mature relational database management system (DBMS). With the rise of main memory database systems, it now has become feasible to also consider applications that built up on linear algebra. This thesis presents a deep integration of linear algebra functionality into an in-memory column-oriented database system. In particular, this work shows that it has become feasible to execute linear algebra queries on large data sets directly in a DBMS-integrated engine (LAPEG), without the need of transferring data and being restricted by hard disc latencies. From various application examples that are cited in this work, we deduce a number of requirements that are relevant for a database system that includes linear algebra functionality. Beside the deep integration of matrices and numerical algorithms, these include optimization of expressions, transparent matrix handling, scalability and data-parallelism, and data manipulation capabilities. These requirements are addressed by our linear algebra engine. In particular, the core contributions of this thesis are: firstly, we show that the columnar storage layer of an in-memory DBMS yields an easy adoption of efficient sparse matrix data types and algorithms. Furthermore, we show that the execution of linear algebra expressions significantly benefits from different techniques that are inspired from database technology. In a novel way, we implemented several of these optimization strategies in LAPEG’s optimizer (SpMachO), which uses an advanced density estimation method (SpProdest) to predict the matrix density of intermediate results. Moreover, we present an adaptive matrix data type AT Matrix to obviate the need of scientists for selecting appropriate matrix representations. The tiled substructure of AT Matrix is exploited by our matrix multiplication to saturate the different sockets of a multicore main-memory platform, reaching up to a speed-up of 6x compared to alternative approaches. Finally, a major part of this thesis is devoted to the topic of data manipulation; where we propose a matrix manipulation API and present different mutable matrix types to enable fast insertions and deletes. We finally conclude that our linear algebra engine is well-suited to process dynamic, large matrix workloads in an optimized way. In particular, the DBMS-integrated LAPEG is filling the linear algebra gap, and makes columnar in-memory DBMS attractive as efficient, scalable ad-hoc analysis platform for scientists